Python is one of the easiest languages to learn and use; it is used widely by beginners and professionals alike. Fortunately, having gone through the basic concepts the user is presented with numerous opportunities to take advanced Python programming courses online. The computing programmes offer these courses since they deal with advanced and specific areas of programming, making the programmers become professional. This article review some of the best online courses and suggest how to choose the right one.
Top Advanced Python Programming Courses Online
- Python Data Science Handbook by Jake VanderPlas (O’Reilly Media)
- Platform: O’Reilly Online Learning
- Description: Thus, this voluminous manual describes the tools and technologies of data science through the use of Python. Application areas discussed here are IPython, NumPy, pandas, Matplotlib, Scikit-Learn and so on. It was developed for learners who wish to advance to specialized topic in data science using Python.
- Pros: Extensive coverage of data science libraries, practical examples, authored by a renowned data scientist.
- Cons: Best suited for those with a strong foundation in Python and some experience in data science.
- Python for Data Science and Machine Learning Bootcamp by Jose Portilla (Udemy)
- Platform: Udemy
- Description: Here both the Data Science and Machine learning are taught under this course but all are done using Python. Here some of the topics they cover include NumPy, pandas, Matplotlib, Seaborn, Plotly, Scikit-learn and TensorFlow. Thus, the course is not only informative but also very practical as the major part of it is carried out through projects.
- Pros: Comprehensive, hands-on projects, highly rated instructor.
- Cons: Requires a significant time commitment, may be overwhelming for those without prior data science knowledge.
- Advanced Python Programming by Joe Marini (LinkedIn Learning)
- Platform: LinkedIn Learning
- Description: This course builds upon topics learned in the previous courses where different topics like decorators, context managers, generators and metaclasses are covered. It will suit best the audience that aims to gain a greater insight into Python, its possibilities, opportunities, and the ways to implement them.
- Pros: Concise and focused, covers advanced concepts thoroughly, accessible via LinkedIn Learning subscription.
- Cons: Limited to Python language features, not application-specific.
- Applied Data Science with Python Specialization (University of Michigan, Coursera)
- Platform: Coursera
- Description: This specialization consists of five courses covering data visualization, machine learning, text analysis, and social network analysis using Python. It is taught by the University of Michigan faculty and includes hands-on projects.
- Pros: University-level instruction, extensive content, hands-on projects.
- Cons: Requires commitment to complete the full specialization, can be challenging for beginners.
- Full Stack Web Development with Python and Django by Dr. Angela Yu (Udemy)
- Platform: Udemy
- Description: This course relates to full stack web development concentrating on Python and Django. This concerns design technologies such as HTML, CSS, and Javascript, among others, and also the back-end using Django. Other area that are covered in the course includes deployment and database management.
- Pros: Comprehensive full stack coverage, practical projects, highly rated instructor.
- Cons: Requires knowledge of web development basics, long duration.
- Python for Everybody Specialization (University of Michigan, Coursera)
- Platform: Coursera
- Description: This specialization is not truly advanced but builds from beginner’s level and includes topics at the advanced level in its later classes. It includes Python introduction, data structures, databases, and scraping.
- Pros: Gradual progression, hands-on projects, university-level instruction.
- Cons: Initial courses may be too basic for some learners.
- Deep Learning Specialization by Andrew Ng (Coursera)
- Platform: Coursera
- Description: This specialization deals with deep learning for computational purposes via programming languages such as Python as well as TensorFlow. It includes the topics like Recurrent Neural Networks, Convolutional Neural Networks, Sequence Models, etc. In fact, one of the most well-known specialists in the field of AI, Andrew Ng, includes it in his course.
- Pros: Expert instruction, comprehensive deep learning coverage, practical assignments.
- Cons: Requires knowledge of machine learning fundamentals, intensive workload.